Data-driven QSPR analysis of anti-cancer drugs using python-based topological techniques

This study proposes a machine learning-based Quantitative Structure–Property Relationship (QSPR) model for predicting the physicochemical properties of anti-cancer drugs by utilizing topological descriptors. The development of anti-cancer drugs poses a significant challenge due to the intricate relationship between drug efficacy and chemical structure. The present study utilizes machine learning regression models in combination with leave-one-out cross-validation (LOOCV) to predict a range of physicochemical properties, including boiling point, enthalpy, molar refractivity, complexity, molecular weight, heavy atom count, flash point, and polarizability. The models are developed using data from thirty anti-cancer drugs and assessed using performance metrics such as the correlation coefficient ( R ), the coefficient of determination ( R 2 ) and root mean square error (RMSE). The findings are encouraging, with a thorough comparison made between the observed values and the values predicted by the QSPR models. • The study focuses on the use of QSPR models to analyze 30 anticancer drugs used in different cancer therapies. • To compute degree-based topological indices (TIs) of anticancer drugs, a Python-based code is developed. • The study develops QSPR models that use degree-based TIs to correlate molecular descriptors with drug properties such as boiling point, enthalpy, molar refractivity, complexity, molecular weight, heavy atom count, flash point, and polarizability. • The study provides a comparative analysis between the linear, quadratic and cubic regression models.